The present invention relates to methods and apparatus for image segmentation for separation of a foreground object from a background. In particular, it relates to interactive 3D segmentation of an object from a background.
Cancer is the second leading cause of death in the United States as half a million people die each year. For this reason, it is important to diagnose the disease as early as possible and monitor it carefully as it is being treated. In addition to primary tumors, physicians are also interested in secondary tumors that might have metastasized through the rest of the body. The main areas of concern are the liver (because many forms of cancer generate liver metastases) and lymph nodes (because cancers spread through the lymphatic system).
The current standards to measure lesions for cancer monitoring are the WHO and RECIST criteria. The first criterion was proposed in 1979 by the World Health Organization in its “WHO handbook for reporting results of cancer treatment,” and measures a tumor in two dimensions by its maximum diameter in an axial image multiplied by its largest perpendicular diameter in the same image.
In 2000, the Response Evaluation Criteria in Solid Tumor (RECIST) in P. Therasse, S. G. Arbuck, E. A. Eisenhauer, J. Wanders, R. S. Kaplan, L. Rubinstein, J. Verweij, M. van Glabbeke, A. T. van Oosterom, M. C. Christian, and S. G. Gwyther, “New guidelines to evaluate the response to treatment in solid tumors,” Journal of the National Cancer Institute, vol. 92, no. 3, pp. 205-216, 2000, stated that the maximum diameter in an axial image alone could be used to quantify tumor sizes. It has been shown, however, that volume measurements provide more accurate estimates of the lesion sizes than one- and two-dimensional criteria as described in L. van Hoe, E. van Cutsem, I. Vergote, A. L. Baert, E. Bellon, P. Dupont, and G. Marchal, “Size quantification of liver metastases in patients undergoing cancer treatment: reproducibility of one-, two-, and three-dimensional measurements determined with spiral CT,” Radiology, vol. 202, no. 3, pp. 671-675, 1997.
A lesion may be characterized in size and/or shape by different characteristics. These characteristics may include: volume, RECIST diameter, orthogonal diameter, WHO criterion, maximum 3D diameter, Z-extent, eccentricity, mean Hounsfield Unit, standard deviation of the Hounsfield Unit. These characteristics may be determined from a 3D segmentation of a lesion in 3D image data. Other characteristics may also be used.
Unfortunately, there are no good tools available to measure lesion volumes and it is very time consuming to manually outline them in 3D. Accordingly, novel and improved methods and systems for segmentation which from a single click inside or a stroke through the lesion can produce a 3D segmentation are required.